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Efficient Methods for Distributed Machine Learning and Resource Management in the Internet-of-Things
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| 자료유형 | 학위논문 |
|---|---|
| 서명/저자사항 | Efficient Methods for Distributed Machine Learning and Resource Management in the Internet-of-Things. |
| 개인저자 | Chen, Tianyi. |
| 단체저자명 | University of Minnesota. Electrical/Computer Engineering. |
| 발행사항 | [S.l.]: University of Minnesota., 2019. |
| 발행사항 | Ann Arbor: ProQuest Dissertations & Theses, 2019. |
| 형태사항 | 204 p. |
| 기본자료 저록 | Dissertations Abstracts International 81-04B. Dissertation Abstract International |
| ISBN | 9781088312186 |
| 학위논문주기 | Thesis (Ph.D.)--University of Minnesota, 2019. |
| 일반주기 |
Source: Dissertations Abstracts International, Volume: 81-04, Section: B.
Advisor: Giannakis, Georgios B. |
| 이용제한사항 | This item must not be sold to any third party vendors. |
| 요약 | Undoubtedly, this century evolves in a world of interconnected entities, where the notion of Internet-of-Things (IoT) plays a central role in the proliferation of linked devices and objects. In this context, the present dissertation deals with large-scale networked systems including IoT that consist of heterogeneous components, and can operate in unknown environments.The focus is on the theoretical and algorithmic issues at the intersection of optimization, machine learning, and networked systems. Specifically, the research objectives and innovative claims include: (T1) Scalable distributed machine learning approaches for efficient IoT implementation |
| 일반주제명 | Computer engineering. Computer science. |
| 언어 | 영어 |
| 바로가기 | 
: 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
